Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+base_model: sentence-transformers/all-mpnet-base-v2
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:505654
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 'module: stationery & printed material & services group: stationery
+    & printed material & services supergroup: stationery & printed material & services
+    example descriptions: munchkin crayons hween printedsheet mask 2 pk printed tape
+    tour os silver butterfly relax with art m ab hardbacknotebook stickers p val youmeyou
+    text heat w mandalorian a 5 nbook nediun bubble envelopes 6 pk whs pastel expan
+    org p poll decoration 1 airtricity payasyoug'
+  sentences:
+  - 'retailer: groveify description: rainbow magicbooks'
+  - 'retailer: crispcorner description: glazed k kreme'
+  - 'retailer: vitalveg description: may held aop fl'
+- source_sentence: 'module: flavoured drinks carbonated cola group: drinks flavoured
+    rtd supergroup: beverages non alcoholic example descriptions: cola w xcoke zero
+    15 oml pepsi 240 k coke zero 500 ml d lepsi max chry 600 coke cherry can 009500
+    pepsi max 500 ml tuo diet coke cf kloke zero coke zero 250 ml diet coke nin 15
+    cocac 3 a 250 ml coca cola 330 ml 10 px coke 125 lzero coke 250 mlreg pmpg 5 p'
+  sentences:
+  - 'retailer: vitalveg description: coke 240 k'
+  - 'retailer: vitalveg description: tala silicone icing'
+  - 'retailer: bountify description: pah antibac wood 10 l'
+- source_sentence: 'module: skin conditioning moisturising group: skin conditioning
+    moisturising supergroup: personal care example descriptions: ss crmy bdy oil dove
+    dm spa sr f m 7 nivea creme 50 carmex lime stick talc powder bo dry skn gel garnier
+    milk bld lpblm orgnl vit a serum nv cr gran oh olay bright eye crm bio oil 2 x
+    200 ml nvfc srm q 10 prlbst sf aa nt crm 50 aveeno cream 500 ml'
+  sentences:
+  - 'retailer: wilko description: radiator m key'
+  - 'retailer: nourify description: okf lprp tblpbl un'
+  - 'retailer: crispcorner description: 065 each fredflo 60 biodegradable'
+- source_sentence: 'module: cakes gateaux ambient group: cakes gateaux ambient supergroup:
+    food ambient example descriptions: x 20 pkmcvitiesjaffacakes 1 srn ban lunchbx
+    js angel slices x 6 spk mr kipling frosty fancies plantastic cherry choc fl hr
+    kipling angel slices 10 pk brompton choc brownies jschocchunknuffin loaded drip
+    cake hobnbchoc fjack oreo muffins x 2 mr kipling victoria slices 6 pack mk kip
+    choc rdsugar m the best brownies odby 5 choc mini'
+  sentences:
+  - 'retailer: flavorful description: nr choc brownies'
+  - 'retailer: producify description: dettol srfc wipe'
+  - 'retailer: noshify description: garden wheels plate'
+- source_sentence: 'module: bread ambient group: bread ambient supergroup: food ambient
+    example descriptions: 1 war 3 toastie 400 g cc 90 varburtons bread tovis snelwrspmpkin
+    800 g warbutons medium bread spk giant crumpets z hovis med wht 600 g sandwich
+    thins 5 pk warb pk crumpets mission plain tortilla 25 cm warburtons 4 protein
+    thin bagels hovis soft wet med hovis wholemefl pataks pappadums 6 pk warb so bth
+    disc pappajuns'
+  sentences:
+  - 'retailer: greenly description: pomodoro sauce'
+  - 'retailer: crispcorner description: kingsmill 5050 medius bread 800 g'
+  - 'retailer: vitalveg description: ready to eat prun'
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: sentence transformers/all mpnet base v2
+      type: sentence-transformers/all-mpnet-base-v2
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.498812351543943
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6342042755344418
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7102137767220903
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7838479809976246
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.498812351543943
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.21140142517814728
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14204275534441804
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07838479809976245
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.498812351543943
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6342042755344418
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7102137767220903
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7838479809976246
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6324346540369431
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5850111224220487
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5910447073012788
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision f1b1b820e405bb8644f5e8d9a3b98f9c9e0a3c58 -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("carnival13/all-mpnet-base-v2-modulepred")
+# Run inference
+sentences = [
+    'module: bread ambient group: bread ambient supergroup: food ambient example descriptions: 1 war 3 toastie 400 g cc 90 varburtons bread tovis snelwrspmpkin 800 g warbutons medium bread spk giant crumpets z hovis med wht 600 g sandwich thins 5 pk warb pk crumpets mission plain tortilla 25 cm warburtons 4 protein thin bagels hovis soft wet med hovis wholemefl pataks pappadums 6 pk warb so bth disc pappajuns',
+    'retailer: crispcorner description: kingsmill 5050 medius bread 800 g',
+    'retailer: vitalveg description: ready to eat prun',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `sentence-transformers/all-mpnet-base-v2`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.4988    |
+| cosine_accuracy@3   | 0.6342    |
+| cosine_accuracy@5   | 0.7102    |
+| cosine_accuracy@10  | 0.7838    |
+| cosine_precision@1  | 0.4988    |
+| cosine_precision@3  | 0.2114    |
+| cosine_precision@5  | 0.142     |
+| cosine_precision@10 | 0.0784    |
+| cosine_recall@1     | 0.4988    |
+| cosine_recall@3     | 0.6342    |
+| cosine_recall@5     | 0.7102    |
+| cosine_recall@10    | 0.7838    |
+| cosine_ndcg@10      | 0.6324    |
+| cosine_mrr@10       | 0.585     |
+| **cosine_map@100**  | **0.591** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### csv
+
+* Dataset: csv
+* Size: 505,654 training samples
+* Columns: <code>query</code> and <code>full_doc</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | full_doc                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 14.8 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 83 tokens</li><li>mean: 115.71 tokens</li><li>max: 176 tokens</li></ul> |
+* Samples:
+  | query                                                         | full_doc                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>retailer: vitalveg description: twin xira</code>        | <code>module: chocolate single variety group: chocolate chocolate substitutes supergroup: biscuits & confectionery & snacks example descriptions: milky way twin 43 crml prtzlarum rai galaxy mnstr pipnut 34 g dark pb cup nest mnch foge p nestle smarties shar dark choc chun x 10 pk kinder bueno 1 dr oetker 72 da poppets choc offee pouch yorkie biscuit zpk haltesers truffles bog cadbury mini snowballs p terrys choc orange 3435 g galaxy fusion dark 704 100 g</code> |
+  | <code>retailer: freshnosh description: mab pop sockt</code>   | <code>module: clothing & personal accessories group: clothing & personal accessories supergroup: clothing & personal accessories example descriptions: pk blue trad ging 40 d 3 pk opaque tight t 74 green cali jogger ss animal swing yb denim stripe pump aw 21 ff vest aw 21 girls 5 pk lounge toplo sku 1 pk fleecy tight knitted pom hat pk briefs timeless double pom pomkids hat cute face twosie sku coral jersey str pun faded petrol t 32 seamfree waist c</code>       |
+  | <code>retailer: nourify description: bts prwn ckt swch</code> | <code>module: bread sandwiches filled rolls wraps group: bread fresh fixed weight supergroup: food perishable example descriptions: us chicken may hamche sw jo dbs allbtr pp st 4 js baconfree ran posh cheesy bea naturify cb swich sp eggcress f cpdfeggbacon js cheeseonion sv duck wrap reduced price takeout egg mayo sandwich 7 takeout cheeseonion s wich 2 ad leicester plough bts cheese pman 2 1 cp bacon chese s</code>                                               |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | sentence-transformers/all-mpnet-base-v2_cosine_map@100 |
+|:------:|:----:|:-------------:|:------------------------------------------------------:|
+| 0.0016 | 100  | 1.6195        | 0.2567                                                 |
+| 0.0032 | 200  | 1.47          | 0.3166                                                 |
+| 0.0047 | 300  | 1.2703        | 0.3814                                                 |
+| 0.0063 | 400  | 1.1335        | 0.4495                                                 |
+| 0.0079 | 500  | 0.9942        | 0.4827                                                 |
+| 0.0095 | 600  | 0.9004        | 0.5058                                                 |
+| 0.0111 | 700  | 0.8838        | 0.5069                                                 |
+| 0.0016 | 100  | 0.951         | 0.5197                                                 |
+| 0.0032 | 200  | 0.9597        | 0.5323                                                 |
+| 0.0047 | 300  | 0.9241        | 0.5406                                                 |
+| 0.0063 | 400  | 0.8225        | 0.5484                                                 |
+| 0.0079 | 500  | 0.7961        | 0.5568                                                 |
+| 0.0095 | 600  | 0.7536        | 0.5621                                                 |
+| 0.0111 | 700  | 0.7387        | 0.5623                                                 |
+| 0.0127 | 800  | 0.7716        | 0.5746                                                 |
+| 0.0142 | 900  | 0.7921        | 0.5651                                                 |
+| 0.0158 | 1000 | 0.7744        | 0.5707                                                 |
+| 0.0174 | 1100 | 0.8021        | 0.5770                                                 |
+| 0.0190 | 1200 | 0.732         | 0.5756                                                 |
+| 0.0206 | 1300 | 0.764         | 0.5798                                                 |
+| 0.0221 | 1400 | 0.7726        | 0.5873                                                 |
+| 0.0237 | 1500 | 0.6676        | 0.5921                                                 |
+| 0.0253 | 1600 | 0.6851        | 0.5841                                                 |
+| 0.0269 | 1700 | 0.7404        | 0.5964                                                 |
+| 0.0285 | 1800 | 0.6798        | 0.5928                                                 |
+| 0.0301 | 1900 | 0.6485        | 0.5753                                                 |
+| 0.0316 | 2000 | 0.649         | 0.5839                                                 |
+| 0.0332 | 2100 | 0.6739        | 0.5891                                                 |
+| 0.0348 | 2200 | 0.6616        | 0.6045                                                 |
+| 0.0364 | 2300 | 0.6287        | 0.5863                                                 |
+| 0.0380 | 2400 | 0.6602        | 0.5898                                                 |
+| 0.0396 | 2500 | 0.5667        | 0.5910                                                 |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.0+cu124
+- Accelerate: 0.33.0
+- Datasets: 2.21.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fef4a662d3113a09d266f04761474f61d9dfa2b5cae7fdf750d36458830a70f
+size 437967672

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
+      "single_word": false,
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+    "30526": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}